Quick Analysis

What is this?

E11.5 (or E11?) mouse RPE

Run on 10x v3 platform

This is a trial run to determine whether Elan can process mouse tissue at this very early time point.

When

2022-05-10

Process Data

Rscript -e 'library(rmarkdown); rmarkdown::render("00_process.Rmd", "html_document")'

Load Data

Quick peek at Elan’s favorite Zfp503 and how which cluster it is differentially expressed in

library(tidyverse)
library(Seurat)
load('../data/00_process_output.Rdata')
seurat.markersCC <- seurat.markersCC %>% bind_rows(.id = 'cluster') %>% as_tibble(rownames = 'Gene') %>% mutate(Gene = gsub('\\.\\.\\.\\d+','',Gene))
# seurat.markersCC %>% 
#   group_by(cluster) %>% slice_max(avg_log2FC, n = 3, with_ties = FALSE) %>% 
#   DT::datatable()
seurat.markersCC %>% bind_rows() %>%  filter(Gene == 'Zfp503')

GO Terms

For each cluster

To help roughly figure out which clusters as which cell types

library(clusterProfiler)
library(enrichplot)
eg_universe = bitr(seuratCC %>% row.names(), fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(seuratCC %>% row.names(), fromType = "SYMBOL", toType = "ENTREZID",  :
  7.96% of input gene IDs are fail to map...

go_enrichCC <- list()
for (i in seurat.markersCC$cluster %>% unique()){
  print(i)
  diff_genes <- seurat.markersCC %>% filter(cluster  == i)
  eg_diff_genes <- bitr(diff_genes$Gene, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Mm.eg.db")
  eg_diff_genes <- diff_genes %>% left_join(., eg_diff_genes, by = c('Gene' = 'SYMBOL'))
  go_enrichCC[[i]] <- enrichGO(gene          = eg_diff_genes$ENTREZID,
                             universe      = eg_universe$ENTREZID,
                             OrgDb         = org.Mm.eg.db,
                             ont           = "all",
                             pAdjustMethod = "BH",
                             pvalueCutoff  = 0.01,
                             qvalueCutoff  = 0.05,
                             readable      = TRUE)
}

[1] "4"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.12% of input gene IDs are fail to map...

[1] "2"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.79% of input gene IDs are fail to map...

[1] "3"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  2.88% of input gene IDs are fail to map...

[1] "0"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  11.86% of input gene IDs are fail to map...

[1] "6"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.96% of input gene IDs are fail to map...

[1] "1"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.92% of input gene IDs are fail to map...

[1] "5"

'select()' returned 1:1 mapping between keys and columns

[1] "7"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  0.48% of input gene IDs are fail to map...

[1] "8"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.6% of input gene IDs are fail to map...

[1] "9"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  4.84% of input gene IDs are fail to map...

[1] "12"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  0.82% of input gene IDs are fail to map...

[1] "10"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.39% of input gene IDs are fail to map...

[1] "13"

'select()' returned 1:1 mapping between keys and columns
Warning in bitr(diff_genes$Gene, fromType = "SYMBOL", toType = "ENTREZID",  :
  1.91% of input gene IDs are fail to map...

[1] "11"

'select()' returned 1:1 mapping between keys and columns

for (i in seurat.markersCC$cluster %>% unique() %>% sort()){
  print(dotplot(go_enrichCC[[i]], showCategory=30) + ggtitle(glue::glue("Dotplot for cluster {i}")))
}

NA
NA

Hand label each cluster (guessing the cell type)

ct_cluster_CC <- rbind(c(0, "RPC"),
  c(1, 'RPC'),
  c(2, 'RPE Precursor'),
  c(3, 'Fibroblast'),
  c(4, 'Fibroblast'),
  c(5, 'Optic Fissure'),
  c(6, 'Proliferating RPC'),
  c(7, 'Epithelial'),
  c(8, 'Red Blood Cell'),
  c(9, 'Corneal Progenitor'),
  c(10, 'Blood Vessel'),
  c(11, "PBMC"),
  c(12, "NK/T"),
  c(13, "Lens"
  )) %>% data.frame()
colnames(ct_cluster_CC) <- c('seurat_clusters','CellType')
seuratCC@meta.data$CellType <- seuratCC@meta.data %>% dplyr::select(seurat_clusters) %>% left_join(ct_cluster_CC) %>% pull(CellType)

Joining, by = "seurat_clusters"

# ct_cluster <- c(0, "RPC",
#   1, 'Fibroblasts',
#   2, 'RPC',
#   3, 'RPE Precursor',
#   4, 'Cornea - JCT - TM?',
#   5, 'Optic Fissure',
#   6, 'Proliferating RPC',
#   7, 'Epithelial',
#   8, 'Red Blood Cell',
#   9, '?',
#   10, 'Vessel',
#   11, "Macrophage/Microglia")

UMAP

With my poorly researched cell type assignments

DimPlot(seuratCC, reduction = "umap", group.by = 'CellType', label = TRUE, label.box = TRUE) + 
  scale_color_manual(values = pals::alphabet2() %>% unname()) +
  scale_fill_manual(values = pals::alphabet2() %>% unname() ) 

Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.

Cell Phase

DimPlot(seuratCC, group.by = 'Phase')

Markers

FeaturePlot(seuratCC, features = c("Pax2", "Ntn1", "Smoc2", "Vax1", 
                                 "Ttr", "Dct", "Rho", "Zfp503", "Mitf",
                                 "Pax6"))

Top 10 Cluster 5 (Optic Fissure?) Diff Genes

top_genes <- seurat.markersCC %>%  
  left_join(seuratCC@meta.data %>% as_tibble() %>% 
              dplyr::select(cluster = seurat_clusters, CellType) %>% unique()) %>% 
  group_by(cluster, CellType) %>% top_n(30, avg_log2FC) %>% 
  filter(cluster == 5) %>% head(15) %>% pull(Gene)

Joining, by = "cluster"

FeaturePlot(seuratCC, features = top_genes)

Zfp503 Like Genes

Zfp503 is overexpressed in both clusters 2 and 5

What other genes have this characteristic?

 seurat.markersCC %>% filter(cluster %in% c(2,5), avg_log2FC > 0.7, p_val_adj < 0.01) %>% 
  group_by(Gene) %>% 
  summarise(Count = n(), avg_log2FC = mean(avg_log2FC)) %>% 
  filter(Count == 2)

FeaturePlot(seuratCC, features = seurat.markersCC %>% filter(cluster %in% c(2,5), avg_log2FC > 0.7, p_val_adj < 0.01) %>% 
  group_by(Gene) %>% 
  summarise(Count = n(), avg_log2FC = mean(avg_log2FC)) %>% 
  filter(Count == 2) %>% 
    pull(Gene))

Cluster diff expressed genes

Top 30 gene for each cluster (I was using this table along with the GO terms to guess each cell type for each cluster)

seurat.markersCC %>%  
  left_join(seuratCC@meta.data %>% as_tibble() %>% 
              dplyr::select(cluster = seurat_clusters, CellType) %>% unique()) %>% 
  group_by(cluster, CellType) %>% top_n(30, avg_log2FC) %>% 
  relocate(CellType) %>% 
  arrange(cluster) %>% 
  DT::datatable()

Joining, by = "cluster"

Quick comments

1. The experiment appears to have worked
2. You seem to have a Optic Fissure specific cluster?? Cluster 5. Several interesting genes in that cluster.

Sesssion Info

devtools::session_info()

─ Session info  🚰  🤴  🇨🇺   ──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
 hash: potable water, prince, flag: Cuba

 setting  value
 version  R version 4.1.2 (2021-11-01)
 os       macOS Catalina 10.15.7
 system   x86_64, darwin17.0
 ui       RStudio
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       America/New_York
 date     2022-05-10
 rstudio  2021.09.0+351 Ghost Orchid (desktop)
 pandoc   2.14.0.3 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (via rmarkdown)

─ Packages ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
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 tidygraph          1.2.0    2020-05-12 [1] CRAN (R 4.1.2)
 tidyr            * 1.1.4    2021-09-27 [1] CRAN (R 4.1.2)
 tidyselect         1.1.1    2021-04-30 [1] CRAN (R 4.1.2)
 tidytree           0.3.6    2021-11-12 [1] CRAN (R 4.1.2)
 tidyverse        * 1.3.1    2021-04-15 [1] CRAN (R 4.1.2)
 treeio             1.18.1   2021-11-14 [1] Bioconductor
 tweenr             1.0.2    2021-03-23 [1] CRAN (R 4.1.2)
 tzdb               0.2.0    2021-10-27 [1] CRAN (R 4.1.2)
 usethis            2.1.3    2021-10-27 [1] CRAN (R 4.1.2)
 utf8               1.2.2    2021-07-24 [1] CRAN (R 4.1.2)
 uwot               0.1.11   2021-12-02 [1] CRAN (R 4.1.2)
 vctrs              0.3.8    2021-04-29 [1] CRAN (R 4.1.2)
 viridis            0.6.2    2021-10-13 [1] CRAN (R 4.1.0)
 viridisLite        0.4.0    2021-04-13 [1] CRAN (R 4.1.2)
 withr              2.4.2    2021-04-18 [1] CRAN (R 4.1.2)
 xfun               0.28     2021-11-04 [1] CRAN (R 4.1.2)
 xml2               1.3.2    2020-04-23 [1] CRAN (R 4.1.0)
 xtable             1.8-4    2019-04-21 [1] CRAN (R 4.1.2)
 XVector            0.34.0   2021-10-26 [1] Bioconductor
 yaml               2.2.1    2020-02-01 [1] CRAN (R 4.1.2)
 yulab.utils        0.0.4    2021-10-09 [1] CRAN (R 4.1.0)
 zlibbioc           1.40.0   2021-10-26 [1] Bioconductor
 zoo                1.8-9    2021-03-09 [1] CRAN (R 4.1.2)

 [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

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